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. 2019 Feb:10949:10.1117/12.2512902.
doi: 10.1117/12.2512902. Epub 2019 Mar 15.

Harmonizing 1.5T/3T Diffusion Weighted MRI through Development of Deep Learning Stabilized Microarchitecture Estimators

Vishwesh Nath  1 , Samuel Remedios  2 , Prasanna Parvathaneni  3 , Colin B Hansen  1 , Roza G Bayrak  1 , Camilo Bermudez  4 , Justin A Blaber  1 , Kurt G Schilling  4 , Vaibhav A Janve  5 , Yurui Gao  5 , Yuankai Huo  1 , Ilwoo Lyu  1 , Owen Williams  6 , Susan Resnick  6 , Lori Beason-Held  6 , Baxter P Rogers  5 , Iwona Stepniewska  7 , Adam W Anderson  4 , Bennett A Landman  1   4   3   5
Affiliations

Harmonizing 1.5T/3T Diffusion Weighted MRI through Development of Deep Learning Stabilized Microarchitecture Estimators

Vishwesh Nath et al. Proc SPIE Int Soc Opt Eng. 2019 Feb.

Abstract

Diffusion weighted magnetic resonance imaging (DW-MRI) is interpreted as a quantitative method that is sensitive to tissue microarchitecture at a millimeter scale. However, the sensitization is dependent on acquisition sequences (e.g., diffusion time, gradient strength, etc.) and susceptible to imaging artifacts. Hence, comparison of quantitative DW-MRI biomarkers across field strengths (including different scanners, hardware performance, and sequence design considerations) is a challenging area of research. We propose a novel method to estimate microstructure using DW-MRI that is robust to scanner difference between 1.5T and 3T imaging. We propose to use a null space deep network (NSDN) architecture to model DW-MRI signal as fiber orientation distributions (FOD) to represent tissue microstructure. The NSDN approach is consistent with histologically observed microstructure (on previously acquired ex vivo squirrel monkey dataset) and scan-rescan data. The contribution of this work is that we incorporate identical dual networks (IDN) to minimize the influence of scanner effects via scan-rescan data. Briefly, our estimator is trained on two datasets. First, a histology dataset was acquired on three squirrel monkeys with corresponding DW-MRI and confocal histology (512 independent voxels). Second, 37 control subjects from the Baltimore Longitudinal Study of Aging (67-95 y/o) were identified who had been scanned at 1.5T and 3T scanners (b-value of 700 s/mm2, voxel resolution at 2.2mm, 30-32 gradient volumes) with an average interval of 4 years (standard deviation 1.3 years). After image registration, we used paired white matter (WM) voxels for 17 subjects and 440 histology voxels for training and 20 subjects and 72 histology voxels for testing. We compare the proposed estimator with super-resolved constrained spherical deconvolution (CSD) and a previously presented regression deep neural network (DNN). NSDN outperformed CSD and DNN in angular correlation coefficient (ACC) 0.81 versus 0.28 and 0.46, mean squared error (MSE) 0.001 versus 0.003 and 0.03, and general fractional anisotropy (GFA) 0.05 versus 0.05 and 0.09. Further validation and evaluation with contemporaneous imaging are necessary, but the NSDN is promising avenue for building understanding of microarchitecture in a consistent and device-independent manner.

Keywords: DW-MRI; Deep Learning; Dual Network; Fiber Orientation Distribution; Harmonization; Null Space; Spherical Harmonics.

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Figures

Figure 1:
Figure 1:
A) Middle axial slice on a 1.5T scanner depicting CSD FOD’s across the WM. B) Middle axial slice on a 3T scanner depicting CSD FOD’s across the WM. C) Angular Correlation Coefficient of the FOD’s which is calculated on corresponding voxels of 1.5T and 3T scanner. Annotations 1,2 and 3 depict low correlation regions of the brain on the left side, same can be observed on the right side.
Figure 2:
Figure 2:
The base regression neural network is depicted in the center which describes the parameters of the fully connected dense layers with respective activation functions. The plot at left: Depicts three inputs where the center input comes from corresponding DW-MRI with histology. The other two are pairwise inputs from corresponding voxels of scanner 1.5T and 3T. The plot at right: Depicts the loss function which uses the hypothesis that the outcome/prediction should be same irrespective of the scanner gradient strength.
Figure 3:
Figure 3:
A) Shows ACC between 1.5T and 3T scanner acquisitions across all WM voxels of 20 withheld subjects for the three methods CSD, DNN and NSDN. B) Shows MSE between 1.5T and 3T scanner acquisitions across all WM voxels of 20 withheld subjects for the three methods CSD, DNN and NSDN. C) Shows difference in GFA between 1.5T and 3T scanner acquisitions across all WM voxels of 20 withheld subjects for the three methods CSD, DNN and NSDN.
Figure 4:
Figure 4:
A, B and C) Depict the individual distribution per subject of ACC across WM voxels between 1.5T and 3T scanners. The blue and orange lines depict the mean and median of ACC across all 20 subjects. D, E and F) Depict the individual distribution per subject of difference in GFA across WM voxels between 1.5T and 3T scanners. G, H and I) Depict the individual distribution per subject of MSE across WM voxels between 1.5T and 3T scanners.
Figure 5:
Figure 5:
Row-wise spatial maps of ACC for the middle axial slice of the brain are depicted for the three methods per columns. ACC improves from left to right with NSDN achieving the highest across all four subjects.
Figure 6:
Figure 6:
A & B) Depicts CSD FOD’s with underlay of ACC on Scanner 1.5T and Scanner 3T data. C & D) Depicts DNN FOD’s with underlay of ACC on Scanner 1.5T and Scanner 3T data. E & F) Depicts NSDN FOD’s with underlay of ACC on 1.5T and 3T. G) The region of interest being observed is the left side frontal lobe WM.
Figure 7:
Figure 7:
A) Middle axial slice on a 1.5T scanner depicting NSDN FOD’s across the WM. B) Middle axial slice on a 3T scanner depicting NSDN FOD’s across the WM. C) ACC of the FOD’s which is calculated on corresponding voxels of 1.5T and 3T scanner. Annotations 1,2 and 3 depict higher correlation regions of the brain on the left side, same can be observed on the right side as compared to Figure 1.
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